



from datetime import datetime

import matplotlib.pyplot as plt
import torch


def A    (							__UpperCamelCase       )    ->   Any:
			for param in module.parameters():
						A__	  =     False


def A    (							)    ->   str:
			A__	  =     'cuda' if torch.cuda.is_available() else 'cpu'
			if torch.backends.mps.is_available() and torch.backends.mps.is_built():
						A__	  =     'mps'
			if device == "mps":
						print(
						    'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
						    ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
						    ' with generations.'       )
			return device


def A    (							__UpperCamelCase       )    ->   int:
			A__	  =     plt.imshow(__UpperCamelCase       )
			fig.axes.get_xaxis().set_visible(__UpperCamelCase       )
			fig.axes.get_yaxis().set_visible(__UpperCamelCase       )
			plt.show()






def A    (							)    ->   Optional[Any]:
			A__	  =     datetime.now()
			A__	  =     current_time.strftime('%H:%M:%S'       )
			return timestamp









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE__     :						Dict	=  {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}


if TYPE_CHECKING:
			from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
			import sys

			SCREAMING_SNAKE_CASE__     :						Dict	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




